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@article{195629,
        author = {Md.Ashique and B.Ramesh and V.Karthik and Sk.Abdul Nayeem},
        title = {Smart Traffic Management System for Emergency Vehicle},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5969-5973},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195629},
        abstract = {Efficient ambulance movement through congested urban roads is a major problem in emergency response systems. To overcome this problem, in the presented work an AI-based Smart Ambulance Detection framework is proposed in which existing CTV (Centralized Traffic Violations) surveillance infrastructure, deep learning and IoT-enabled traffic control are used. The system uses a camera-based detection method with a YOLOv5 object detection model and OpenCV for the frame processing part to accurately detect ambulances in real time based on video detection. Upon successful detection, a backend Flask based triggers and automated alert will communicate to a microcontroller unit such as ESP32 or Arduino to enable dynamic modification of traffic signals states to prioritize ambulances route. The solution does not require any further on-vehicle hardware which simplifies easy deployment and cost-effectiveness. Experimental results demonstrate that the system performs reliably in varied lighting and traffic circumstances thus showing the potential to greatly lessen emergency response delays and improve the efficiency when transporting traffic in urban areas.},
        keywords = {YOLOv5, Computer Vision, CCTV Surveillance, OpenCV, Deep Learning,Signal Automation.},
        month = {April},
        }